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Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)

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Synopses & Reviews

Publisher Comments:

Machines capable of automatic pattern recognition have many fascinating uses in science and engineering as well as in our daily lives. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. Tremendous progress has been made in refining such algorithms; yet, automatic learning in many simple tasks in daily life still appears to be far from reach. This book takes a close view of data complexity and its role in shaping the theories and techniques in different disciplines and asks: • What is missing from current classification techniques? • When the automatic classifiers are not perfect, is it a deficiency of the algorithms by design, or is it a difficulty intrinsic to the classification task? • How do we know whether we have exploited to the fullest extent the knowledge embedded in the training data? Data Complexity in Pattern Recognition is unique in its comprehensive coverage and multidisciplinary approach from various methodological and practical perspectives. Researchers and practitioners alike will find this book an insightful reference to learn about the current status of available techniques as well as application areas.

Synopsis:

Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.

Table of Contents

Theory and Methodology.- Measures of Geometrical Complexity in Classification Problems.- Object Representation, Sample Size and Dataset Complexity.- Measures of Data and Classifier Complexity and the Training Sample Size.- Linear Separability in Descent Procedures for Linear Classifiers.- Data Complexity, Margin-based Learning and Popper's Philosophy of Inductive Learning.- Data Complexity and Evolutionary Learning.- Data Complexity and Domains of Competence of Classifiers.- Data Complexity Issues in Grammatical Inference.- Applications.- Simple Statistics for Complex Feature Spaces.- Polynomial Time for Complexity Graph Distance Computation for Web Content Mining.- Data Complexity in Clustering Analysis for Gene Microarray Expression Profiles.- Complexity of Magnetic Resonance Spectrum Classification.- Data Complexity in Tropical Cyclone Positioning and Classification.- Human-Computer Interaction for Complex Pattern Recognition Problems.- Complex Image Recognition and Web Security.

Product Details

ISBN:
9781849965576
Author:
Basu, Mitra
Publisher:
Springer
Author:
Ho, Tin Kam
Location:
London
Subject:
Computers-Reference - General
Subject:
Computer Vision
Subject:
Data Complexity
Subject:
Discrimination Analysis
Subject:
Neural Networks
Subject:
Pattern recognition.
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Algorithm Analysis and Problem Complexity
Subject:
Computer Science
Subject:
Language, literature and biography
Subject:
Optical pattern recognition
Subject:
Artificial Intelligence
Subject:
Computer software
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2006
Series:
Advanced Information and Knowledge Processing
Publication Date:
20101231
Binding:
TRADE PAPER
Language:
English
Pages:
316
Dimensions:
235 x 155 mm 477 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Personal Computers » General
Health and Self-Help » Health and Medicine » Medical Specialties
Transportation » Automotive » General

Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing) New Trade Paper
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Product details 316 pages Not Avail - English 9781849965576 Reviews:
"Synopsis" by , Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.
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